AI-Driven Healthcare: A Review on Ensuring Fairness and Mitigating Bias

TL;DR

This paper reviews AI in healthcare, focusing on ethical challenges of bias and fairness, and discusses strategies like diverse datasets and fairness-aware algorithms to promote equitable healthcare.

Contribution

It provides a comprehensive overview of bias issues in healthcare AI and proposes strategies for mitigation and ensuring fairness in AI-driven medical applications.

Findings

Bias in healthcare AI can lead to disparities in treatment outcomes.

Diverse datasets and fairness-aware algorithms are essential for equitable AI healthcare.

Regulatory frameworks and transparency are crucial for ethical AI deployment.

Abstract

Artificial intelligence (AI) is rapidly advancing in healthcare, enhancing the efficiency and effectiveness of services across various specialties, including cardiology, ophthalmology, dermatology, emergency medicine, etc. AI applications have significantly improved diagnostic accuracy, treatment personalization, and patient outcome predictions by leveraging technologies such as machine learning, neural networks, and natural language processing. However, these advancements also introduce substantial ethical and fairness challenges, particularly related to biases in data and algorithms. These biases can lead to disparities in healthcare delivery, affecting diagnostic accuracy and treatment outcomes across different demographic groups. This review paper examines the integration of AI in healthcare, highlighting critical challenges related to bias and exploring strategies for mitigation. We emphasize the necessity of diverse datasets, fairness-aware algorithms, and regulatory frameworks to ensure equitable healthcare delivery. The paper concludes with recommendations for future research, advocating for interdisciplinary approaches, transparency in AI decision-making, and the development of innovative and inclusive AI applications.